Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift

TL;DR

This paper introduces JointPGM, a probabilistic graphical model that captures intra- and inter-series correlations and models distribution shifts in non-stationary multivariate time series, leading to improved forecasting accuracy.

Contribution

The paper proposes a novel neural framework, JointPGM, that jointly models intra-/inter-series dynamics and distribution shifts using Fourier basis functions and probabilistic inference.

Findings

Achieves state-of-the-art performance on six non-stationary MTS datasets.

Effectively captures temporal and spatial dynamics in non-stationary data.

Demonstrates robustness against distribution shifts in multivariate time series.

Abstract

The non-stationary nature of real-world Multivariate Time Series (MTS) data presents forecasting models with a formidable challenge of the time-variant distribution of time series, referred to as distribution shift. Existing studies on the distribution shift mostly adhere to adaptive normalization techniques for alleviating temporal mean and covariance shifts or time-variant modeling for capturing temporal shifts. Despite improving model generalization, these normalization-based methods often assume a time-invariant transition between outputs and inputs but disregard specific intra-/inter-series correlations, while time-variant models overlook the intrinsic causes of the distribution shift. This limits model expressiveness and interpretability of tackling the distribution shift for MTS forecasting. To mitigate such a dilemma, we present a unified Probabilistic Graphical Model to Jointly capturing intra-/inter-series correlations and modeling the time-variant transitional distribution, and instantiate a neural framework called JointPGM for non-stationary MTS forecasting. Specifically, JointPGM first employs multiple Fourier basis functions to learn dynamic time factors and designs two distinct learners: intra-series and inter-series learners. The intra-series learner effectively captures temporal dynamics by utilizing temporal gates, while the inter-series learner explicitly models spatial dynamics through multi-hop propagation, incorporating Gumbel-softmax sampling. These two types of series dynamics are subsequently fused into a latent variable, which is inversely employed to infer time factors, generate final prediction, and perform reconstruction. We validate the effectiveness and efficiency of JointPGM through extensive experiments on six highly non-stationary MTS datasets, achieving state-of-the-art forecasting performance of MTS forecasting.